Assignment 4: Introduction to Pix4D

Introduction
     The purpose of this assignment is introduce the Pix4D software. This software is used to process unmaned aerial systems data (UAS). This software allows for the processing of point clouds and allows for 3D volume analysis. In order to use  Pix4D, it is very important that the user has high quality data sets. This can incude the use of ground control points (GCPs), geolocation and quality imagery.
     When using using Pix4D it is very important that the UAS data has overlap. It is recommended that a there is a minimum of 75% percent of frontal and 65% of lateral overlap for most situations. If the UAS is flying over over surfaces such as snow and or sand, there needs to be increased overlap for the images. In these cases, it is recommended that the overlap should be increased to 85% frontal and 70% lateral. Pix4D is also capable of producing oblique images, if there is sufficient overlap between the images. Images that were taken over the course of multiple flights can also be processed. This is as long as the total images is below 2000 images. If using images taken from multiple flights, similar flight conditions are desired and overlap between images is very important.
     Rapid Check is also a very important function in Pix4D. This function is used to determine if there is sufficient coverage for the images in the data set. It performs this function quickly by reducing the size of the pixels in the images so that processing speed can increased.
     As mentioned above, Pix4D can use GCPs. While this is not required, it is recommended. Having accurate GCPs aids the quality and accuracy of the overlap between images. GCPs also helpful for other tasks such as processing images without geolocation as well as georeferencing.
     Once an image has been processed the software produces a quality report that contains information on the accuracy of the data collection.

Demostration 
Volume Calculations 
    Pix4D has the ability to calculate volumes of 3D surfaces. This can be done by using the volume tool found in the menu. Once the tool is selected, control points can be placed on the processed image. Once the desired control points have been placed on the image, by simply right-clicking the mouse, the volume can be calculated. This can be seen below, where the volume for 3 gravel pits were calculated using this tool (fig. 1).
    

Figure 1. Three volumes collected using the volume tool

Flyover
     Pix4D can also create animations for data sets. For this example a video was created that "flew" above the processed image. This was done by using the video tool where way points can be placed around the image. This effectively creates a flight path for the animation. Once the video's way-points are collected, the video can be rendered into video formats such mp4 for exportation.

ArcMap
    Data processed in Pix4D can also be brought into ArcMap. For this lab, two maps were created, a digital surface model (DSM) (fig.2 )and orthomosic image (fig, 3). The images for this lab were preprocessed by Dr. Hupy. The DSM and orthomosic images were brought into ArcMap as raster features. For the DSM, a hillshade was created to create better depth in the image using the hillshade tool. Once the hillshade layer was created the DSM was placed over the hillshade and was made 30% percent transparent. The DSM was also imported in ArcScene to create a 3D model. These can be seen in the upper right of two maps.

Figure 2. Displays the DSM brought into ArcMap

Figure 3. Displays the orthomosiac image

Conclusion
      Pix4D is a very effective software for processing UAS images and data sets. When processing UAS data it is important to follow the recommended overlap percentages so that the data can be processed accurately. The software provides many useful tools for calculating volumes, displaying animations, and creation of data that be used in mapping software such as ArcMap.

Navigation Maps

Introduction
     For this lab we were asked to create two naviagtion maps for a future lab using different coordinate systems and map projections. The coordinate systems used were the WGS_1984_UTM_Zone_15N and NAD_1983_HARN_WISCRS_EauClaire_County_Feet were used for the two maps. This lab was done to show how different coordinate systems and projections can be used to make navigation maps. Proper coordinate system and map projection choice is important for creating navigation maps, because if an improper would create a distorted map and therefore hard to navigate. Geographic coordinate systems create a three-dimensional model and map projections create a two-dimensional model to portray the Earth's Surface.

Methods
     We were given a mosaiced raster image that displayed the study area. Before the images could be brought into ArcMap, the map documents were projected into the Mercator projection. The images were then reprojected into their different coordinate systems,  UTM (fig. 1) and HARN. To create elevation contour lines for the two images, the Contour Tool was then used to create a contour intervals for the different maps, 10 feet and 3 meters respectively.

Figure 1. UTM Zones for the United States

     Once the contour lines were created for each of the images, grid systems were then created for each of the two maps. For the UTM map a measured grid was created. This was done under the Layer Data Frame Property Tab. The interval spacing was set at 100 meters. The HARN map used a grapicule grid as it was in decimal degrees, with spacing every 5 degrees.


Results
     The two maps below show the study area contained within the red-outlined box. The contours (maroon) allow for viariabilities in the elevation within the priory to be examined. The different grid systems allow for the locations of places within the priory to be accurately plotted. When looking at the maps, areas in the southwest corner are at a higher elevation than areas in the northeast. Also areas in the western and southeastern corners are at a lower elevation than areas in the southwest.

Figure 2.  The map created in the UTM coordinate system using a measured grid.

Figure 3. Map created using the HARN coordinate system in decimal degrees

Conclusion
     The maps above are displaying the same area but they are doing so in two different ways. Each way provides method for navigating the study area. When creating navigation maps it is important to consider the effects that using different projected and geographic coordinate systems can have on the map as it will effect the ability to navigation accurately.

Sources
http://www.xmswiki.com/wiki/File:Usutm.png

Sandbox Survey

Introduction
     In the previous lab, we collect elevation points for a 114x114 cm sandbox. The data points were collected using a systematic sampling method collecting sample points every 6 cm within the grid. Once the elevation data was recorded it was then transfered into an excel spreadsheet. The data then needed to be normalized to decreased error and ease processing. Normalization refers to cleaning up data so that it uniform and easy to work with. Because a systematic sampling method was chosen for the initial survey, the data was already organized in evenly spaced increments. This allowed for the normalization to be very easily (figure 1).
     The objective for this lab was to take the topology data collected in the previous lab and create 3D topographic profiles in ArcMap and ArcScene. This was done using five interpolation methods 1) spline, 2) IDW, 3) natural neighbor, 4) kriging, and 5) Tin. Each of the methods produce different results when given the same data, because of this each of the interpolation methods will be explained in further detail below.

Figure. 1 Excel sheet displaying the normalized data collected in the previous lab

Methods
     Before the data could be interpolated into 3D topographic profiles, the data needed to be imported into ArcMap. To do this the add X,Y data tool was used create a new shapefile within a newly created feature class in a new geodatabase. The data for this project was left unprojected because the data was not collected using a geographic coordinate system, rather our own coordinate system (see lab 1 for details). Once this was completed the data could then be interpolated. After being interpolated, the models were brought into ArcScene where they were turned into 3D models.

• Spline: The first interpolation method was spline. Spline uses mathematical estimate values that reduce the curvature of a surface, passing through the center of the data points. This results a profile with a smooth surface. Spline is an effective method when there are a lot of data points but is not optimal if there are few data points as the model tends to over-correct, resulting in a overly simplified profile built upon generalizations. If there are large discrepancies in the elevation of data points that are close together, the model struggles to create realistic profiles. 
• (Inverse Distance Weighted (IDW): The IDW method estimates cell values by averaging the values of the collected data points using a weighted scale based upon relative distance to the sample point. For example, if a cell is closer to the sample point, it will have a higher weight assigned to that cell in comparison to a cell further away.
• Natural Neighbor: This method places a strong importance on the sample points themselves and creates regions surronding each point. 
• Kriging: This method is more complex than the previous methods as it uses formulas to create an estimated surface based upon sample point values. This model takes into consideration the correlations between direction and distance to predict a surface.  
• Triangulated Irregular Network (TIN): Tin models are created using set of vertices (sample points) to create a triangulated network using Delaunay triangulation. TIN models create high resolution areas where there is high amounts of variability between points and lower resolution models where data points have low variability. 

     Once the models were run, 2D topographic profiles were created, where they were later imported into ArcScene to create 3D topographic profiles.

Results
Spline: The first interpolation method was spline (figure 2). This created a smooth surface that accurately portrayed the surface of the sandbox. Areas in the southwest corner were over generalized as they were more flat in sandbox than the model portrayed. Of the five interpolation methods, this produced the most aesthetically pleasing model.

Figure 2. Spline 3D interpolation model

IDW: The second model used was the IDW interpolation method (figure 3). This model does portray the elevation changes in the sand box quite well, however the model fails to smooth the surface. This produces a model that has unusual looking bumps that make the model appear unrealistic.

Figure 3. IDW interpolation 3D model

Natural Neighbor: The next interpolation method used was natural neighbors (figure 4). This method produced a smooth surface that portrayed the surface accurately. Although the model is smooth, it lacks detail in areas of higher elevation changes than other models used.

Figure 4. Natural Neighbor 3D model

Kriging: The next model run was kriging (figure 5). This model gives a very basic profile of the sandbox. While the general surface of the model gives a general idea of what the topology of the sand box looked like, however the model leaves a lot to be desired in terms of detail.

Figure 5. Kriging 3D interpolation model

TIN: The final method used was TIN (figure 6). This method accurately portrayed the various elevations very accurately and provided an accurate representation of the sandbox. The model however doesn't portray the surface accurately as the triangulations give the model a more jagged look than the topology of the sandbox.

Figure 6. TIN  3D interpolation model

Summary
     Each of the methods used to create the 3D topographic profiles created unique models as each used different methods to achieve their final result. Of the five, the spline method created the most accurate representation of the sandbox. This was because of systematic sampling method combined with of samples relatively small area, allowed for the model to create a very accurate profile. Some of the other sampling methods would have been more appropriate had the study area been larger a more broad sampling method been applied. Interpolation models are not limited only to elevation models, but can be applied to precipitation, temperature, and even air pollution models.

Assignment 1: Survey Grids for Digital Terrain Models

Introduction

Often in the field, the cost to collect all the information for a particular area can be very costly in both time and fiscal cost. To combat this, the method of sampling is used as a shortcut to achieving successful spatial analysis while saving cost and time. For this exercise our class was given the task to collect topographic data for sandbox. The sandbox topography was created by each of the classes individual groups. Sampling can be conducted in three main methods 1) random, 2) systematic and 3) stratified. Of the three sampling methods, random sampling produces the least amount of user bias as each sample of a given population has an equal chance of being selected and is selected at random. Problems with random sampling method is that certain portions of the population are not included in the survey resulting in error. Stratified sampling consists of a population being divided into proportional categories based upon similar characteristics or zones.  Systematic sampling is conducted in an organized structure such as a coordinate grid to collect samples at equal intervals. This can lead to errors as areas that do not correspond to grid intervals will not be sampled. The purpose of this lab is to create a topographic profile that will be used later to create a digital terrain model (DTM).

Methods

After weighing the pros and cons of the three sampling methods our group decided to use the systematic point sampling method. The reason we chose to conduct our survey using the systematic method was because we could divide the sample into an evenly spaced grid allowing for the survey to be consistent throughout the survey.

To conduct our survey we used the following materials: sandbox filled with sand, yarn, pushpins, a meter stick, field notebook, pencil and a tape measure (Figure 1.). The inside dimensions of the sandbox measured 114 cm by 114 cm and push pins were placed every 6 cm around the sandbox resulting in 19 evenly placed markers on each side of the box. Once the pins were put into place, we then designed the topography of the sandbox creating multiple features such as a ridge, valley and plane.

Figure 1 A tape measure, pushpins, field notebook and meter stick were used to complete the survey

To conduct our systematic sampling of the sandbox, we created a coordinate system using strings yarn attached to pins spaced every 6 cm to create our X lines (Figure 2). We used a measuring tape to create a straight line for the Y lines as we conducted our survey. The measuring tape was moved further down the grid as the survey was being conducted. We then took elevations measurements (Z) every 6 cm along the Y lines using a meter stick where the X lines intersected the Y lines. The height of the yarn across the grid was our measuring point for the elevation.

Figure 2 Our grid created for our systematic sampling method. Yarn was placed for the X lines spaced every 6 cm. A tape measure (lower right) served as our Y line and was moved throught the grid as we progressed through the survey.

The data was then recorded into a field notebook noting each points X and Y location their corresponding Z values. Once all sampling points were collected they were imported into excel.

Figure 3 Displays the collected elevation measurements along with their corresponding location with in the grid 

Results

We collected a total of 401 sampling points throughout the sandbox where the X and Y lines intersected. Using excel we then calculated the maximum elevation (lowest point), minimum elevation (highest point), mean and standard deviation of the sample (shown below).

Standard Deviation: 2.3874
Mean: -5.9325
Max: -12.5
Min: 0

Using the systematic sampling method, we were able to create an equal interval grid system that allowed for us to collect a large amount of sample points in an organized manor. This made the final results of the sample to be organized in a clear and simple layout (Figure 4.). Using this method alone did however leave some areas with the grid to not be sample as we collected Z values at the corners of the grid. This left areas between the intersections of the grid to be left unsampled. This sample could have been aided through the use of a stratified system in areas of uniformity and sharper elevation change in order to achieve higher levels of accuracy. Along with the lack of multiple methods being used, we also had errors in our elevation data due to the meter stick sinking into the sand when measuring, leading to errors that varied from about 1-2 cm.

Figure 4. The table displays the elevations of different sample points and their locations throughout the sample grid.

Conclusions

The sampling used in the lab relates to the definition of sampling because points were collected in locations specified by a grid system placed over the survey area. This acted as a shortcut in our survey as points weren't collected for every location in the sandbox. Using sampling in a spatial situation allows for the surveyed area to be conducted in a timely manor. It would be impossible to collect data points everywhere within the sandbox and would consume extraordinary amounts of time and effort. This relates to sampling of larger spatial areas because surveyors in the field do not have the time nor the budget to collect survey points at every location within their fields of study which often far exceed the scale conducted in this exercise. To improve the resolution of our sampling a decreased our sampling interval from every 6 cm to every 3 cm could have been used and would have created a survey with a higher point density.